Zobrazeno 1 - 10
of 41
pro vyhledávání: '"Dexin Sun"'
Publikováno v:
Applied Sciences, Vol 14, Iss 11, p 4433 (2024)
This study introduces the Adversarial Task Augmented Sequential Meta-Learning (ATASML) framework, designed to enhance fault diagnosis in industrial processes. ATASML integrates adversarial learning with sequential task learning to improve the model
Externí odkaz:
https://doaj.org/article/1c8525fc227a47aaa71a62efb6a9b235
Autor:
Yaozhong Zhang, Han Zhang, Hengxing Lan, Yunchuang Li, Honggang Liu, Dexin Sun, Erhao Wang, Zhonghong Dong
Publikováno v:
Water, Vol 16, Iss 8, p 1133 (2024)
Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their ac
Externí odkaz:
https://doaj.org/article/88c88a75ac4a47bd94a8cc954cfbbef1
Publikováno v:
Applied Sciences, Vol 14, Iss 1, p 181 (2023)
In this paper, we propose the gradient-oriented prioritization meta-learning (GOPML) algorithm, a new approach for few-shot fault diagnosis in industrial systems. The GOPML algorithm utilizes gradient information to prioritize tasks, aiming to improv
Externí odkaz:
https://doaj.org/article/5cc9e98c249841018af17bffab49eff2
Publikováno v:
Applied Sciences, Vol 13, Iss 21, p 11678 (2023)
Deadbeat predictive current control (DPCC) has excellent dynamics and can achieve current control with less computational effort. However, its control performance relies on the precision of the parameters of the motor. Current static error will be ge
Externí odkaz:
https://doaj.org/article/16bf9559880f4a2289cc7018ce55bc00
Publikováno v:
Remote Sensing, Vol 15, Iss 21, p 5174 (2023)
Hyperspectral imaging often suffers from various types of noise, including sensor non-uniformity and atmospheric disturbances. Removing multiple types of complex noise in hyperspectral images (HSIs) while preserving high fidelity in spectral dimensio
Externí odkaz:
https://doaj.org/article/1dfcc7ef6d43465e997e40df4b92e195
Publikováno v:
Applied Sciences, Vol 13, Iss 9, p 5549 (2023)
The performance of long-wave infrared (LWIR) quantum well (QWIP) detection systems is seriously affected by the dark current of the detectors. Tiny variations in the focal-plane temperature of the devices cause fluctuations in the dark current, which
Externí odkaz:
https://doaj.org/article/a01b5ae8c37f445ebbb7f50bc5ace4e9
Publikováno v:
Micromachines, Vol 14, Iss 4, p 713 (2023)
An active optical system with three segmented mirrors was proposed to verify the co-focus and co-phase progress. In this system, a kind of large-stroke and high-precision parallel positioning platform was specially developed to help support the mirro
Externí odkaz:
https://doaj.org/article/93b093a224f04394a8ca7dab39b91e46
Autor:
Zhentao Gong, Haoting Du, Wenming Wu, Kehan Chen, Jiang Tian, Chengsheng Ji, Dexin Sun, Yinnian Liu
Publikováno v:
Applied Sciences, Vol 13, Iss 6, p 3389 (2023)
The coupling relationship between space electronics systems is complex, and the signals of optoelectronic load cables are susceptible to interference, especially the early anomalous weak signals on a ground surface used for immediate remote sensing,
Externí odkaz:
https://doaj.org/article/e53dd5f41d544dca9b2b44bd5383a5d6
Publikováno v:
Applied Sciences, Vol 12, Iss 24, p 12967 (2022)
The long-wave infrared (LWIR) quantum-well photodetector (QWIP) operates at low temperatures, but is prone to focal plane temperature changes when imaging in complex thermal environments. This causes dark current changes and generates low-frequency t
Externí odkaz:
https://doaj.org/article/36fd82c6e5414ab8a5c42746cbdae124
Publikováno v:
Remote Sensing, Vol 10, Iss 1, p 79 (2018)
Dispersive hyperspectral VNIR (visible and near-infrared) imagers using back-illuminated CCDs will suffer from interference fringes in near-infrared bands, which can cause a sensitivity modulation as high as 40% or more when the spectral resolution g
Externí odkaz:
https://doaj.org/article/5408b10ab47f4788a5d312d5820b5c1c